As the population ages, the number of careers that intersect with aging is expected to grow. However, many young people lack an interest in working with aging populations. As previous work has shown, though, students’ interest in aging careers may be stimulated by coursework and experiential activities related to aging. Despite being a normative developmental process, anxiety about death and dying may be particular barriers to students developing interest in aging, and these topics may be particularly difficult subjects to teach in the college classroom. Here, strategies and activities for teaching the end of life are offered.

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L1, L2, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.